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A novel ultra-short-term wind power prediction method based on XA mechanism

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  • Peng, Cheng
  • Zhang, Yiqin
  • Zhang, Bowen
  • Song, Dan
  • Lyu, Yi
  • Tsoi, AhChung

Abstract

A major difficulty in integrating large scale wind power generation in an electrical power system is that wind generated power appears to be erratic, intermittent, and volatile. In this paper, we demonstrate the efficacies of a novel ultra short term 1-step ahead wind generated power prediction model, by combining two best of breed machine learning models in their respective areas of applications: a deep convolutional neural network (CNN) model, known to be effective in classification problems, and a bi-directional long short term memory (Bi-LSTM) model, known to be effective in 1-step ahead time series prediction problems, using a cross attention (XA) mechanism on three challenging practical datasets: the East-China dataset, the Yalova (Turkey) dataset, and the 16 MW dataset.

Suggested Citation

  • Peng, Cheng & Zhang, Yiqin & Zhang, Bowen & Song, Dan & Lyu, Yi & Tsoi, AhChung, 2023. "A novel ultra-short-term wind power prediction method based on XA mechanism," Applied Energy, Elsevier, vol. 351(C).
    • Handle: RePEc:eee:appene:v:351:y:2023:i:c:s0306261923012692
    DOI: 10.1016/j.apenergy.2023.121905
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    References listed on IDEAS

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    1. Farah, Shahid & David A, Wood & Humaira, Nisar & Aneela, Zameer & Steffen, Eger, 2022. "Short-term multi-hour ahead country-wide wind power prediction for Germany using gated recurrent unit deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
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